LinearRegressionSummary (Spark 2.2.1 JavaDoc)

Object
- org.apache.spark.ml.regression.LinearRegressionSummary

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

LinearRegressionTrainingSummary
```
public class LinearRegressionSummary
extends Object
implements scala.Serializable
```
:: Experimental :: Linear regression results evaluated on a dataset.
param: predictions predictions output by the model's transform method. param: predictionCol Field in "predictions" which gives the predicted value of the label at each instance. param: labelCol Field in "predictions" which gives the true label of each instance. param: featuresCol Field in "predictions" which gives the features of each instance as a vector.

See Also:

Serialized Form

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double[]`	`coefficientStandardErrors()` Standard error of estimated coefficients and intercept.
`long`	`degreesOfFreedom()` Degrees of freedom
`double[]`	`devianceResiduals()` The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
`double`	`explainedVariance()` Returns the explained variance regression score.
`String`	`featuresCol()`
`String`	`labelCol()`
`double`	`meanAbsoluteError()` Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
`double`	`meanSquaredError()` Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
`long`	`numInstances()` Number of instances in DataFrame predictions
`String`	`predictionCol()`
`Dataset<Row>`	`predictions()`
`double[]`	`pValues()` Two-sided p-value of estimated coefficients and intercept.
`double`	`r2()` Returns R^2^, the coefficient of determination.
`Dataset<Row>`	`residuals()` Residuals (label - predicted value)
`double`	`rootMeanSquaredError()` Returns the root mean squared error, which is defined as the square root of the mean squared error.
`double[]`	`tValues()` T-statistic of estimated coefficients and intercept.

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - predictions
```
public Dataset<Row> predictions()
```
  - predictionCol
```
public String predictionCol()
```
  - labelCol
```
public String labelCol()
```
  - featuresCol
```
public String featuresCol()
```
  - explainedVariance
```
public double explainedVariance()
```
    Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: Wikipedia explain variation
    
    Returns:
    
    (undocumented)
    
    Note:
    
    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
  - meanAbsoluteError
```
public double meanAbsoluteError()
```
    Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
    
    Returns:
    
    (undocumented)
    
    Note:
    
    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
  - meanSquaredError
```
public double meanSquaredError()
```
    Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
    
    Returns:
    
    (undocumented)
    
    Note:
    
    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
  - rootMeanSquaredError
```
public double rootMeanSquaredError()
```
    Returns the root mean squared error, which is defined as the square root of the mean squared error.
    
    Returns:
    
    (undocumented)
    
    Note:
    
    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
  - r2
```
public double r2()
```
    Returns R^2^, the coefficient of determination. Reference: Wikipedia coefficient of determination
    
    Returns:
    
    (undocumented)
    
    Note:
    
    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
  - residuals
```
public Dataset<Row> residuals()
```
    Residuals (label - predicted value)
  - numInstances
```
public long numInstances()
```
    Number of instances in DataFrame predictions
  - degreesOfFreedom
```
public long degreesOfFreedom()
```
    Degrees of freedom
  - devianceResiduals
```
public double[] devianceResiduals()
```
    The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
    
    Returns:
    
    (undocumented)
  - coefficientStandardErrors
```
public double[] coefficientStandardErrors()
```
    Standard error of estimated coefficients and intercept. This value is only available when using the "normal" solver.
    If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.
    
    Returns:
    
    (undocumented)
    
    See Also:
    
    LinearRegression.solver
  - tValues
```
public double[] tValues()
```
    T-statistic of estimated coefficients and intercept. This value is only available when using the "normal" solver.
    If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.
    
    Returns:
    
    (undocumented)
    
    See Also:
    
    LinearRegression.solver
  - pValues
```
public double[] pValues()
```
    Two-sided p-value of estimated coefficients and intercept. This value is only available when using the "normal" solver.
    If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.
    
    Returns:
    
    (undocumented)
    
    See Also:
    
    LinearRegression.solver

Class LinearRegressionSummary

Method Summary

Methods inherited from class Object

Method Detail

predictions

predictionCol

labelCol

featuresCol

explainedVariance

meanAbsoluteError

meanSquaredError

rootMeanSquaredError

r2

residuals

numInstances

degreesOfFreedom

devianceResiduals

coefficientStandardErrors

tValues

pValues